README.md

• Minuet AI
• Features
• Requirements
• Installation
• Configuration
• API Keys
• Prompt
• Providers
  • OpenAI
  • Claude
  • Codestral
  • Gemini
  • OpenAI-compatible
  • OpenAI-FIM-compatible
  • Huggingface
• Commands
  • Minuet change_provider, Minuet change_model
  • Minuet blink, Minuet cmp
  • Minuet virtualtext
• API
  • Virtual Text
• FAQ
  • Customize cmp ui
  • Significant Input Delay When Moving to a New Line
  • Integration with lazyvim
• TODO
• Contributing
• Acknowledgement

Minuet AI

Minuet AI: Dance with Intelligence in Your Code 💃.

Minuet-ai brings the grace and harmony of a minuet to your coding process. Just as dancers move during a minuet.

Features

• AI-powered code completion with dual modes:
  • Specialized prompts and various enhancements for chat-based LLMs on code completion tasks.
  • Fill-in-the-middle (FIM) completion for compatible models (DeepSeek, Codestral, and others).
• Support for multiple AI providers (OpenAI, Claude, Gemini, Codestral, Huggingface, and OpenAI-compatible services)
• Customizable configuration options
• Streaming support to enable completion delivery even with slower LLMs
• Support nvim-cmp, blink-cmp, virtual text frontend

With nvim-cmp / blink-cmp frontend:

With virtual text frontend:

Requirements

• Neovim 0.10+.
• plenary.nvim
• optional: nvim-cmp
• optional: blink.cmp
• An API key for at least one of the supported AI providers

Installation

Lazy

specs = {
    {
        'milanglacier/minuet-ai.nvim',
        config = function()
            require('minuet').setup {
                -- Your configuration options here
            }
        end,
    },
    { 'nvim-lua/plenary.nvim' },
    -- optional, if you are using virtual-text frontend, nvim-cmp is not
    -- required.
    { 'hrsh7th/nvim-cmp' },
    -- optional, if you are using virtual-text frontend, blink is not required.
    { 'Saghen/blink.cmp' },
}

Given the response speed and rate limits of LLM services, we recommend you either invoke minuet completion manually or use a cost-effective model like gemini-flash or codestral for auto-completion.

Setting up with virtual text:

require('minuet').setup {
    virtualtext = {
        auto_trigger_ft = {},
        keymap = {
            -- accept whole completion
            accept = '<A-A>',
            -- accept one line
            accept_line = '<A-a>',
            -- accept n lines (prompts for number)
            accept_n_lines = '<A-z>',
            -- Cycle to prev completion item, or manually invoke completion
            prev = '<A-[>',
            -- Cycle to next completion item, or manually invoke completion
            next = '<A-]>',
            dismiss = '<A-e>',
        },
    },
}

Setting up with nvim-cmp:

require('cmp').setup {
    sources = {
        {
             -- Include minuet as a source to enable autocompletion
            { name = 'minuet' },
            -- and your other sources
        }
    },
    performance = {
        -- It is recommended to increase the timeout duration due to
        -- the typically slower response speed of LLMs compared to
        -- other completion sources. This is not needed when you only
        -- need manual completion.
        fetching_timeout = 2000,
    },
}


-- If you wish to invoke completion manually,
-- The following configuration binds `A-y` key
-- to invoke the configuration manually.
require('cmp').setup {
    mapping = {
        ["<A-y>"] = require('minuet').make_cmp_map()
        -- and your other keymappings
    },
}

Setting up with blink-cmp:

require('blink-cmp').setup {
    keymap = {
        -- Manually invoke minuet completion.
        ['<A-y>'] = require('minuet').make_blink_map(),
    },
    sources = {
         -- Enable minuet for autocomplete
        default = { 'lsp', 'path', 'buffer', 'snippets', 'minuet' },
        -- For manual completion only, remove 'minuet' from default
        providers = {
            minuet = {
                name = 'minuet',
                module = 'minuet.blink',
                score_offset = 8, -- Gives minuet higher priority among suggestions
            },
        },
    },
}

LLM Provider Examples:

Fireworks (llama-3.3-70b):

require('minuet').setup {
    provider = 'openai_compatible',
    provider_options = {
        openai_compatible = {
            api_key = 'FIREWORKS_API_KEY',
            end_point = 'https://api.fireworks.ai/inference/v1/chat/completions',
            model = 'accounts/fireworks/models/llama-v3p3-70b-instruct',
            name = 'Fireworks',
            optional = {
                max_tokens = 256,
                top_p = 0.9,
            },
        },
    },
}

Deepseek:

-- you can use deepseek with both openai_fim_compatible or openai_compatible provider
require('minuet').setup {
    provider = 'openai_fim_compatible',
    provider_options = {
        openai_fim_compatible = {
            api_key = 'DEEPSEEK_API_KEY',
            name = 'deepseek',
            optional = {
                max_tokens = 256,
                top_p = 0.9,
            },
        },
    },
}


-- or
require('minuet').setup {
    provider = 'openai_compatible',
    provider_options = {
        openai_compatible = {
            end_point = 'https://api.deepseek.com/v1/chat/completions',
            api_key = 'DEEPSEEK_API_KEY',
            name = 'deepseek',
            optional = {
                max_tokens = 256,
                top_p = 0.9,
            },
        },
    },
}

Ollama:

require('minuet').setup {
    provider = 'openai_fim_compatible',
    n_completions = 1, -- recommend for local model for resource saving
    provider_options = {
        openai_fim_compatible = {
            api_key = 'TERM',
            name = 'Ollama',
            end_point = 'http://localhost:11434/v1/completions',
            model = 'qwen2.5-coder:14b',
            optional = {
                max_tokens = 256,
                top_p = 0.9,
            },
        },
    },
}

Configuration

Minuet AI comes with the following defaults:

default_config = {
    -- Enable or disable auto-completion. Note that you still need to add
    -- Minuet to your cmp/blink sources. This option controls whether cmp/blink
    -- will attempt to invoke minuet when minuet is included in cmp/blink
    -- sources. This setting has no effect on manual completion; Minuet will
    -- always be enabled when invoked manually. You can use the command
    -- `MinuetToggle` to toggle this option.
    cmp = {
        enable_auto_complete = true,
    },
    blink = {
        enable_auto_complete = true,
    },
    virtualtext = {
        -- Specify the filetypes to enable automatic virtual text completion,
        -- e.g., { 'python', 'lua' }. Note that you can still invoke manual
        -- completion even if the filetype is not on your auto_trigger_ft list.
        auto_trigger_ft = {},
        -- specify file types where automatic virtual text completion should be
        -- disabled. This option is useful when auto-completion is enabled for
        -- all file types i.e., when auto_trigger_ft = { '*' }
        auto_trigger_ignore_ft = {},
        keymap = {
            accept = nil,
            accept_line = nil,
            accept_n_lines = nil,
            -- Cycle to next completion item, or manually invoke completion
            next = nil,
            -- Cycle to prev completion item, or manually invoke completion
            prev = nil,
            dismiss = nil,
        },
    },
    provider = 'codestral',
    -- the maximum total characters of the context before and after the cursor
    -- 16000 characters typically equate to approximately 4,000 tokens for
    -- LLMs.
    context_window = 16000,
    -- when the total characters exceed the context window, the ratio of
    -- context before cursor and after cursor, the larger the ratio the more
    -- context before cursor will be used. This option should be between 0 and
    -- 1, context_ratio = 0.75 means the ratio will be 3:1.
    context_ratio = 0.75,
    throttle = 1000, -- only send the request every x milliseconds, use 0 to disable throttle.
    -- debounce the request in x milliseconds, set to 0 to disable debounce
    debounce = 400,
    -- Control notification display for request status
    -- Notification options:
    -- false: Disable all notifications (use boolean false, not string "false")
    -- "debug": Display all notifications (comprehensive debugging)
    -- "verbose": Display most notifications
    -- "warn": Display warnings and errors only
    -- "error": Display errors only
    notify = 'warn',
    -- The request timeout, measured in seconds. When streaming is enabled
    -- (stream = true), setting a shorter request_timeout allows for faster
    -- retrieval of completion items, albeit potentially incomplete.
    -- Conversely, with streaming disabled (stream = false), a timeout
    -- occurring before the LLM returns results will yield no completion items.
    request_timeout = 3,
    -- If completion item has multiple lines, create another completion item
    -- only containing its first line. This option only has impact for cmp and
    -- blink. For virtualtext, no single line entry will be added.
    add_single_line_entry = true,
    -- The number of completion items encoded as part of the prompt for the
    -- chat LLM. For FIM model, this is the number of requests to send. It's
    -- important to note that when 'add_single_line_entry' is set to true, the
    -- actual number of returned items may exceed this value. Additionally, the
    -- LLM cannot guarantee the exact number of completion items specified, as
    -- this parameter serves only as a prompt guideline.
    n_completions = 3,
    -- Defines the length of non-whitespace context after the cursor used to
    -- filter completion text. Set to 0 to disable filtering.
    --
    -- Example: With after_cursor_filter_length = 3 and context:
    --
    -- "def fib(n):\n|\n\nfib(5)" (where | represents cursor position),
    --
    -- if the completion text contains "fib", then "fib" and subsequent text
    -- will be removed. This setting filters repeated text generated by the
    -- LLM. A large value (e.g., 15) is recommended to avoid false positives.
    after_cursor_filter_length = 15,
    -- proxy port to use
    proxy = nil,
    provider_options = {
        -- see the documentation in each provider in the following part.
    },
    -- see the documentation in the `Prompt` section
    default_template = {
        template = '...',
        prompt = '...',
        guidelines = '...',
        n_completion_template = '...',
    },
    default_fim_template = {
        default_prompt = '...',
        default_suffix = '...',
    },
    default_few_shots = { '...' },
}

API Keys

Minuet AI requires API keys to function. Set the following environment variables:

• OPENAI_API_KEY for OpenAI
• GEMINI_API_KEY for Gemini
• ANTHROPIC_API_KEY for Claude
• CODESTRAL_API_KEY for Codestral
• HF_API_KEY for Huggingface
• Custom environment variable for OpenAI-compatible services (as specified in your configuration)

Note: Provide the name of the environment variable to Minuet, not the actual value. For instance, pass OPENAI_API_KEY to Minuet, not the value itself (e.g., sk-xxxx).

If using Ollama, you need to assign an arbitrary, non-null environment variable as a placeholder for it to function.

Prompt

See prompt for the default prompt used by minuet and instructions on customization.

Note that minuet employs two distinct prompt systems:

1. A system designed for chat-based LLMs (OpenAI, OpenAI-Compatible, Claude, and Gemini)
2. A separate system designed for Codestral and OpenAI-FIM-compatible models

Providers

OpenAI

the following is the default configuration for OpenAI:

provider_options = {
    openai = {
        model = 'gpt-4o-mini',
        system = "see [System Prompt] section for the default value",
        few_shots = "see [System Prompt] section for the default value",
        chat_input = "See [Prompt Section for default value]",
        stream = true,
        optional = {
            -- pass any additional parameters you want to send to OpenAI request,
            -- e.g.
            -- stop = { 'end' },
            -- max_tokens = 256,
            -- top_p = 0.9,
        },
    },
}

The following configuration is not the default, but recommended to prevent request timeout from outputing too many tokens.

provider_options = {
    openai = {
        optional = {
            max_tokens = 256,
        },
    },
}

Claude

the following is the default configuration for Claude:

provider_options = {
    claude = {
        max_tokens = 512,
        model = 'claude-3-5-haiku-20241022',
        system = "see [System Prompt] section for the default value",
        few_shots = "see [System Prompt] section for the default value",
        chat_input = "See [Prompt Section for default value]",
        stream = true,
        optional = {
            -- pass any additional parameters you want to send to claude request,
            -- e.g.
            -- stop_sequences = nil,
        },
    },
}

Codestral

Codestral is a text completion model, not a chat model, so the system prompt and few shot examples does not apply. Note that you should use the CODESTRAL_API_KEY, not the MISTRAL_API_KEY, as they are using different endpoint. To use the Mistral endpoint, simply modify the end_point and api_key parameters in the configuration.

the following is the default configuration for Codestral:

provider_options = {
    codestral = {
        model = 'codestral-latest',
        end_point = 'https://codestral.mistral.ai/v1/fim/completions',
        api_key = 'CODESTRAL_API_KEY',
        stream = true,
        template = {
            prompt = "See [Prompt Section for default value]",
            suffix = "See [Prompt Section for default value]",
        },
        optional = {
            stop = nil, -- the identifier to stop the completion generation
            max_tokens = nil,
        },
    },
}

The following configuration is not the default, but recommended to prevent request timeout from outputing too many tokens.

provider_options = {
    codestral = {
        optional = {
            max_tokens = 256,
            stop = { '\n\n' },
        },
    },
}

Gemini

The following config is the default.

provider_options = {
    gemini = {
        model = 'gemini-1.5-flash-latest',
        system = "see [System Prompt] section for the default value",
        few_shots = "see [System Prompt] section for the default value",
        chat_input = "See [Prompt Section for default value]",
        stream = true,
        optional = {},
    },
}

The following configuration is not the default, but recommended to prevent request timeout from outputing too many tokens. You can also adjust the safety settings following the example:

provider_options = {
    gemini = {
        optional = {
            generationConfig = {
                maxOutputTokens = 256,
            },
            safetySettings = {
                {
                    -- HARM_CATEGORY_HATE_SPEECH,
                    -- HARM_CATEGORY_HARASSMENT
                    -- HARM_CATEGORY_SEXUALLY_EXPLICIT
                    category = 'HARM_CATEGORY_DANGEROUS_CONTENT',
                    -- BLOCK_NONE
                    threshold = 'BLOCK_ONLY_HIGH',
                },
            },
        },
    },
}

OpenAI-compatible

Use any providers compatible with OpenAI's chat completion API.

For example, you can set the end_point to http://localhost:11434/v1/chat/completions to use ollama.

Note that not all openAI compatible services has streaming support, you should change stream=false to disable streaming in case your services do not support it.

The following config is the default.

provider_options = {
    openai_compatible = {
        model = 'llama-3.3-70b-versatile',
        system = "see [System Prompt] section for the default value",
        few_shots = "see [System Prompt] section for the default value",
        chat_input = "See [Prompt Section for default value]",
        end_point = 'https://api.groq.com/openai/v1/chat/completions',
        api_key = 'GROQ_API_KEY',
        name = 'Groq',
        stream = true,
        optional = {
            stop = nil,
            max_tokens = nil,
        },
    }
}

OpenAI-FIM-compatible

Use any provider compatible with OpenAI's completion API. This request uses the text completion API, not chat completion, so system prompts and few-shot examples are not applicable.

For example, you can set the end_point to http://localhost:11434/v1/completions to use ollama.

Refer to the Completions Legacy section of the OpenAI documentation for details.

Note that not all openAI compatible services has streaming support, you should change stream=false to disable streaming in case your services do not support it.

Please note that not all OpenAI-compatible services support streaming. If your service does not support streaming, you should set stream=false to disable it.

Additionally, for Ollama users, it is essential to verify whether the model's template supports FIM completion. For example, qwen2.5-coder's template is a supported model. However if may come as a surprise to some users that, deepseek-coder does not support the FIM template, and you should use deepseek-coder-v2 instead.

provider_options = {
    openai_fim_compatible = {
        model = 'deepseek-chat',
        end_point = 'https://api.deepseek.com/beta/completions',
        api_key = 'DEEPSEEK_API_KEY',
        name = 'Deepseek',
        stream = true,
        template = {
            prompt = "See [Prompt Section for default value]",
            suffix = "See [Prompt Section for default value]",
        },
        optional = {
            stop = nil,
            max_tokens = nil,
        },
    }
}

The following configuration is not the default, but recommended to prevent request timeout from outputing too many tokens.

provider_options = {
    openai_fim_compatible = {
        optional = {
            max_tokens = 256,
            stop = { '\n\n' },
        },
    },
}

Huggingface

Currently only text completion model in huggingface is supported, so the system prompt and few shot examples does not apply.

provider_options = {
    huggingface = {
        end_point = 'https://api-inference.huggingface.co/models/bigcode/starcoder2-3b',
        type = 'completion',
        strategies = {
            completion = {
                markers = {
                    prefix = '<fim_prefix>',
                    suffix = '<fim_suffix>',
                    middle = '<fim_middle>',
                },
                strategy = 'PSM', -- PSM, SPM or PM
            },
        },
        optional = {
            parameters = {
                -- The parameter specifications for different LLMs may vary.
                -- Ensure you specify the parameters after reading the API
                -- documentation.
                stop = nil,
                max_tokens = nil,
                do_sample = nil,
            },
        },
    },
}

Commands

Minuet change_provider, Minuet change_model

The change_provider command allows you to change the provider after Minuet has been setup.

Example usage: Minuet change_provider claude

The change_model command allows you to change both the provider and model in one command. The format is provider:model.

Example usage: Minuet change_model gemini:gemini-1.5-pro-latest

Note: For openai_compatible and openai_fim_compatible providers, the model completions in cmdline are determined by the name field in your configuration. For example, if you configured:

provider_options.openai_compatible.name = 'Fireworks'

When entering Minuet change_model openai_compatible: in the cmdline, you'll see model completions specific to the Fireworks provider.

Minuet blink, Minuet cmp

Enable or disable autocompletion for nvim-cmp or blink.cmp. While Minuet must be added to your cmp/blink sources, this command only controls whether Minuet is triggered during autocompletion. The command does not affect manual completion behavior - Minuet remains active and available when manually invoked.

Example usage: Minuet blink toggle, Minuet blink enable, Minuet blink disable

Minuet virtualtext

Enable or disable the automatic display of virtual-text completion in the current buffer.

Example usage: Minuet virtualtext toggle, Minuet virtualtext enable, Minuet virtualtext disable.

API

Virtual Text

minuet-ai.nvim offers the following functions to customize your key mappings:

{
    -- accept whole completion
    require('minuet.virtualtext').action.accept,
    -- accept by line
    require('minuet.virtualtext').action.accept_line,
    -- accept n lines (prompts for number)
    require('minuet.virtualtext').action.accept_n_lines,
    require('minuet.virtualtext').action.next,
    require('minuet.virtualtext').action.prev,
    require('minuet.virtualtext').action.dismiss,
    -- whether the virtual text is visible in current buffer
    require('minuet.virtualtext').action.is_visible,
}

FAQ

Customize cmp ui

You can configure the icons of minuet by using the following snippet (referenced from cmp's wiki):

local cmp = require('cmp')
cmp.setup {
  formatting = {
    format = function(entry, vim_item)
      -- Kind icons
      vim_item.kind = string.format('%s %s', kind_icons[vim_item.kind], vim_item.kind) -- This concatenates the icons with the name of the item kind
      -- Source
      vim_item.menu = ({
        minuet = "󱗻"
      })[entry.source.name]
      return vim_item
    end
  },
}

Significant Input Delay When Moving to a New Line

When using Minuet with auto-complete enabled, you may occasionally experience a noticeable delay when pressing <CR> to move to the next line. This occurs because Minuet triggers autocompletion at the start of a new line, while cmp blocks the <CR> key, awaiting Minuet's response.

To address this issue, consider the following solutions:

1. Unbind the <CR> key from your cmp keymap.
2. Utilize cmp's internal API to avoid blocking calls, though be aware that this API may change without prior notice.

Here's an example of the second approach using Lua:

local cmp = require 'cmp'
opts.mapping = {
    ['<CR>'] = cmp.mapping(function(fallback)
        -- use the internal non-blocking call to check if cmp is visible
        if cmp.core.view:visible() then
            cmp.confirm { select = true }
        else
            fallback()
        end
    end),
}

Integration with lazyvim

With nvim-cmp:

{
    'milanglacier/minuet-ai.nvim',
    config = function()
        require('minuet').setup {
            -- Your configuration options here
        }
    end
},
{
    'nvim-cmp',
    optional = true,
    opts = function(_, opts)
        -- if you wish to use autocomplete
        table.insert(opts.sources, 1, {
            name = 'minuet',
            group_index = 1,
            priority = 100,
        })

        opts.performance = {
            -- It is recommended to increase the timeout duration due to
            -- the typically slower response speed of LLMs compared to
            -- other completion sources. This is not needed when you only
            -- need manual completion.
            fetching_timeout = 2000,
        }

        opts.mapping = vim.tbl_deep_extend('force', opts.mapping or {}, {
            -- if you wish to use manual complete
            ['<A-y>'] = require('minuet').make_cmp_map(),
        })
    end,
}

With blink-cmp:

-- set the following line in your config/options.lua
vim.g.lazyvim_blink_main = true

{
    'milanglacier/minuet-ai.nvim',
    config = function()
        require('minuet').setup {
            -- Your configuration options here
        }
    end,
},
{
    'saghen/blink.cmp',
    optional = true,
    opts = {
        keymap = {
            ['<A-y>'] = {
                function(cmp)
                    cmp.show { providers = { 'minuet' } }
                end,
            },
        },
        sources = {
            -- if you want to use auto-complete
            default =  { 'minuet' },
            providers = {
                minuet = {
                    name = 'minuet',
                    module = 'minuet.blink',
                    score_offset = 100,
                },
            },
        },
    },
}

TODO

1. Implement RAG on the codebase and encode the codebase information into the request to LLM.

Contributing

Contributions are welcome! Please feel free to submit a Pull Request.

Acknowledgement

• cmp-ai: Reference for the integration with nvim-cmp.
• continue.dev: not a neovim plugin, but I find a lot LLM models from here.
• copilot.lua: Reference for the virtual text frontend.